Exchanging Dual Encoder-Decoder: A New Strategy for Change Detection with Semantic Guidance and Spatial Localization

Change detection is a critical task in earth observation applications. Recently, deep learning-based methods have shown promising performance and are quickly adopted in change detection. However, the widely used multiple encoder and single decoder (MESD) as well as dual encoder-decoder (DED) architectures still struggle to effectively handle change detection well. The former has problems of bitemporal feature interference in the feature-level fusion, while the latter is inapplicable to intraclass change detection and multiview building change detection. To solve these problems, we propose a new strategy with an exchanging dual encoder-decoder structure for binary change detection with semantic guidance and spatial localization. The proposed strategy solves the problems of bitemporal feature inference in MESD by fusing bitemporal features in the decision level and the inapplicability in DED by determining changed areas using bitemporal semantic features. We build a binary change detection model based on this strategy, and then validate and compare it with 18 state-of-the-art change detection methods on six datasets in three scenarios, including intraclass change detection datasets (CDD, SYSU), single-view building change detection datasets (WHU, LEVIR-CD, LEVIR-CD+) and a multiview building change detection dataset (NJDS). The experimental results demonstrate that our model achieves superior performance with high efficiency and outperforms all benchmark methods with F1-scores of 97.77%, 83.07%, 94.86%, 92.33%, 91.39%, 74.35% on CDD, SYSU, WHU, LEVIR-CD, LEVIR- CD+, and NJDS datasets, respectively. The code of this work will be available at https://github.com/NJU-LHRS/official-SGSLN

Node Embedding from Neural Hamiltonian Orbits in Graph Neural Networks

In the graph node embedding problem, embedding spaces can vary significantly for different data types, leading to the need for different GNN model types. In this paper, we model the embedding update of a node feature as a Hamiltonian orbit over time. Since the Hamiltonian orbits generalize the exponential maps, this approach allows us to learn the underlying manifold of the graph in training, in contrast to most of the existing literature that assumes a fixed graph embedding manifold with a closed exponential map solution. Our proposed node embedding strategy can automatically learn, without extensive tuning, the underlying geometry of any given graph dataset even if it has diverse geometries. We test Hamiltonian functions of different forms and verify the performance of our approach on two graph node embedding downstream tasks: node classification and link prediction. Numerical experiments demonstrate that our approach adapts better to different types of graph datasets than popular state-of-the-art graph node embedding GNNs. The code is available at \url{https://github.com/zknus/Hamiltonian-GNN}

On the Robustness of Graph Neural Diffusion to Topology Perturbations

Neural diffusion on graphs is a novel class of graph neural networks that has attracted increasing attention recently. The capability of graph neural partial differential equations (PDEs) in addressing common hurdles of graph neural networks (GNNs), such as the problems of over-smoothing and bottlenecks, has been investigated but not their robustness to adversarial attacks. In this work, we explore the robustness properties of graph neural PDEs. We empirically demonstrate that graph neural PDEs are intrinsically more robust against topology perturbation as compared to other GNNs. We provide insights into this phenomenon by exploiting the stability of the heat semigroup under graph topology perturbations. We discuss various graph diffusion operators and relate them to existing graph neural PDEs. Furthermore, we propose a general graph neural PDE framework based on which a new class of robust GNNs can be defined. We verify that the new model achieves comparable state-of-the-art performance on several benchmark datasets

Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach

Graph neural networks (GNNs) are vulnerable to adversarial perturbations, including those that affect both node features and graph topology. This paper investigates GNNs derived from diverse neural flows, concentrating on their connection to various stability notions such as BIBO stability, Lyapunov stability, structural stability, and conservative stability. We argue that Lyapunov stability, despite its common use, does not necessarily ensure adversarial robustness. Inspired by physics principles, we advocate for the use of conservative Hamiltonian neural flows to construct GNNs that are robust to adversarial attacks. The adversarial robustness of different neural flow GNNs is empirically compared on several benchmark datasets under a variety of adversarial attacks. Extensive numerical experiments demonstrate that GNNs leveraging conservative Hamiltonian flows with Lyapunov stability substantially improve robustness against adversarial perturbations. The implementation code of experiments is available at https://github.com/zknus/NeurIPS-2023-HANG-Robustness.Comment: Accepted by Advances in Neural Information Processing Systems (NeurIPS), New Orleans, USA, Dec. 2023, spotligh

GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction

This paper aims to efficiently enable Large Language Models (LLMs) to use multimodal tools. Advanced proprietary LLMs, such as ChatGPT and GPT-4, have shown great potential for tool usage through sophisticated prompt engineering. Nevertheless, these models typically rely on prohibitive computational costs and publicly inaccessible data. To address these challenges, we propose the GPT4Tools based on self-instruct to enable open-source LLMs, such as LLaMA and OPT, to use tools. It generates an instruction-following dataset by prompting an advanced teacher with various multi-modal contexts. By using the Low-Rank Adaptation (LoRA) optimization, our approach facilitates the open-source LLMs to solve a range of visual problems, including visual comprehension and image generation. Moreover, we provide a benchmark to evaluate the ability of LLMs to use tools, which is performed in both zero-shot and fine-tuning ways. Extensive experiments demonstrate the effectiveness of our method on various language models, which not only significantly improves the accuracy of invoking seen tools, but also enables the zero-shot capacity for unseen tools. The code and demo are available at https://github.com/StevenGrove/GPT4Tools

Changes in Alprazolam Metabolism by CYP3A43 Mutants

Alprazolam is a triazolobenzodiazepine which is most commonly used in the short-term management of anxiety disorders, often in combination with antipsychotics. The four human members of the CYP3A subfamily are mainly responsible for its metabolism, which yields the main metabolites 4-hydroxyalprazolam and α-hydroxyalprazolam. We performed a comparison of alprazolam metabolism by all four CYP3A enzymes upon recombinant expression in the fission yeast Schizosaccharomyces pombe. CYP3A4 and CYP3A5 show the highest 4-hydroxyalprazolam production rates, while CYP3A5 alone is the major producer of α-hydroxyalprazolam. For both metabolites, CYP3A7 and CYP3A43 show lower activities. Computational simulations rationalize the difference in preferred oxidation sites observed between the exemplary enzymes CYP3A5 and CYP3A43. Investigations of the alprazolam metabolites formed by three previously described CYP3A43 mutants (L293P, T409R, and P340A) unexpectedly revealed that they produce 4-hydroxy-, but not α-hydroxyalprazolam. Instead, they all also make a different metabolite, which is 5-N-O alprazolam. With respect to 4-hydroxyalprazolam, the mutants showed fourfold (T409R) to sixfold (L293P and P340A) higher production rates compared to the wild-type (CYP3A43.1). In the case of 5-N-O alprazolam, the production rates were similar for the three mutants, while no formation of this metabolite was found in the wild-type incubation

Effects of environmental factors on vertical distribution of the eukaryotic plankton community in early summer in Danjiangkou Reservoir, China

IntroductionEukaryotic plankton plays crucial roles in ecosystem processes, impacting aquatic ecosystem stability. This study focuses on Danjiangkou Reservoir, a canyon lake in central China, that acts as the water source of the Mid-route of the South-to-North Water Diversion Project.MethodsIn this study, high-throughput 18S rDNA gene sequencing was employed to investigate eukaryotic plankton community at four water depths (0.5 m, 5 m, 10 m, and 20 m). The environmental factors including pH, water temperature (WT), nitrate nitrogen (NO3−-N), ammonia nitrogen (NH4+-N), total nitrogen (TN), conductivity (Cond), and dissolved oxygen (DO) in reservoir areas were measured, and their correlations with abundance and diversity of eukaryotic plankton were analyzed.ResultsThe results showed the presence of 122 genera of eukaryotic plankton from 38 phyla. Eukaryotic plankton communities were mainly composed of Eurytemora, Thermocyclops, Sinocalanus, Mesocyclops, and Cryptomonas. In particular, significant differences in the diversity of eukaryotic plankton communities were found in vertical distribution. The diversity and abundance of eukaryotic plankton communities in 7 sampling sites decreased with the increase of depth from 0.5 to 10 m, while the diversity and abundance of plankton communities increased at 20 m. RDA analysis indicated that pH, depth, WT, NH4+-N, DO, Cond, and NO3−-N could influence the vertical distribution of the eukaryotic plankton community in the Danjiangkou Reservoir. Among these eukaryotic plankton, Eurytemora, Thermocyclops, and Volvox were negatively correlated with pH and WT and positively correlated with depth.DiscussionThis study revealed a novel perspective on the distribution of the eukaryotic plankton community in Danjiangkou Reservoir, particularly in terms of vertical variation, which will be helpful to comprehensively understand ecological processes and to further ensure the water quality safety in this canyon-style reservoir

Discrimination of homocysteine, cysteine and glutathione using an aggregation-induced-emission-active hemicyanine dye

Elevated levels of homocysteine (Hcy) in blood are indicative of many high risk cardiovascular and neurodegenerative diseases. Thus, development of highly efficient and selective dyes for monitoring Hcy levels has attracted much attention. This paper reports the utilization of TPE-Cy, an aggregation-induced-emission active hemicyanine dye, as a probe for the detection of Hcy. More interestingly, this dye shows high selectivity to Hcy over cysteine, glutathione and other amino acids in weakly basic buffer solution

Association of platelet-to-lymphocyte ratio and neutrophil-to-lymphocyte ratio with outcomes in stroke patients achieving successful recanalization by endovascular thrombectomy

ObjectiveSerum inflammatory biomarkers play crucial roles in the development of acute ischemic stroke (AIS). In this study, we explored the association between inflammatory biomarkers including platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), and monocyte-to-lymphocyte ratio (MLR), and clinical outcomes in AIS patients who achieved successful recanalization.MethodsPatients with AIS who underwent endovascular thrombectomy (EVT) and achieved a modified thrombolysis in the cerebral infarction scale of 2b or 3 were screened from a prospective cohort at our institution between January 2013 and June 2021. Data on blood parameters and other baseline characteristics were collected. The functional outcome was an unfavorable outcome defined by a modified Rankin Scale of 3–6 at the 3-month follow up. Other clinical outcomes included symptomatic intracranial hemorrhage (sICH) and 3-month mortality. Multivariable logistic regression analysis was performed to evaluate the effects of PLR, NLR, and MLR on clinical outcomes.ResultsA total of 796 patients were enrolled, of which 89 (11.2%) developed sICH, 465 (58.4%) had unfavorable outcomes at 3 months, and 168 (12.1%) died at the 3-month follow up. After adjusting for confounding variables, a higher NLR (OR, 1.076; 95% confidence interval [CI], 1.037–1.117; p < 0.001) and PLR (OR, 1.001; 95%CI, 1.000–1.003; p = 0.045) were significantly associated with unfavorable outcomes, the area under the receiver operating characteristic curve of NLR and PLR was 0.622 and 0.564, respectively. However, NLR, PLR, and MLR were not independently associated with sICH and 3-month mortality (all adjusted p > 0.05).ConclusionOverall, our results indicate that higher PLR and NLR were independently associated with unfavorable functional outcomes in AIS patients with successful recanalization after EVT; however, the underlying mechanisms are yet to be elucidated

Efficient and ultra-stable perovskite light-emitting diodes

Perovskite light-emitting diodes (PeLEDs) have emerged as a strong contender for next-generation display and information technologies. However, similar to perovskite solar cells, the poor operational stability remains the main obstacle toward commercial applications. Here we demonstrate ultra-stable and efficient PeLEDs with extraordinary operational lifetimes (T50) of 1.0x10^4 h, 2.8x10^4 h, 5.4x10^5 h, and 1.9x10^6 h at initial radiance (or current densities) of 3.7 W/sr/m2 (~5 mA/cm2), 2.1 W/sr/m2 (~3.2 mA/cm2), 0.42 W/sr/m2 (~1.1 mA/cm2), and 0.21 W/sr/m2 (~0.7 mA/cm2) respectively, and external quantum efficiencies of up to 22.8%. Key to this breakthrough is the introduction of a dipolar molecular stabilizer, which serves two critical roles simultaneously. First, it prevents the detrimental transformation and decomposition of the alpha-phase FAPbI3 perovskite, by inhibiting the formation of lead and iodide intermediates. Secondly, hysteresis-free device operation and microscopic luminescence imaging experiments reveal substantially suppressed ion migration in the emissive perovskite. The record-long PeLED lifespans are encouraging, as they now satisfy the stability requirement for commercial organic LEDs (OLEDs). These results remove the critical concern that halide perovskite devices may be intrinsically unstable, paving the path toward industrial applications.Comment: This is a preprint of the paper prior to peer review. New and updated results may be available in the final version from the publishe